{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e3032edc-e99d-45a7-a809-c88bd24df870",
   "metadata": {},
   "source": [
    "# <span style=\"color: #9333EA;\">单值圆环图</span>\n",
    "## 一.导入数据\n",
    "### 筛选完旗舰店和专营店的订单数量之后，绘制一个单值圆环图，直观表示旗舰店订单数量和专营店订单数量在这总订单中的占比。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "42ed17f5-ba10-46e4-8565-47e75ea24bc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "========================================\n",
      "ERP订单数据 - 包含旗舰店的店铺 商品类别订单数量统计\n",
      "========================================\n",
      "  裙子:  71 个订单\n",
      "  毛衣:  54 个订单\n",
      "  卫衣:  63 个订单\n",
      "  裤子:  66 个订单\n",
      "  夹克:  55 个订单\n",
      "  外套:  63 个订单\n",
      "  T恤:  73 个订单\n",
      "  衬衫:  66 个订单\n",
      "========================================\n",
      "  总计: 511 个订单\n",
      "========================================\n",
      "ERP订单数据 - 包含专营店的店铺 商品类别订单数量统计\n",
      "========================================\n",
      "  裙子:  49 个订单\n",
      "  毛衣:  53 个订单\n",
      "  卫衣:  64 个订单\n",
      "  裤子:  68 个订单\n",
      "  夹克:  71 个订单\n",
      "  外套:  60 个订单\n",
      "  T恤:  61 个订单\n",
      "  衬衫:  63 个订单\n",
      "========================================\n",
      "  总计: 489 个订单\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# 读取 Excel 文件\n",
    "data = pd.read_excel(\"ERP订单数据.xlsx\")\n",
    "\n",
    "target_categories = {\n",
    "    \"裙子\": \"裙子\",\n",
    "    \"毛衣\": \"毛衣\",\n",
    "    \"卫衣\": \"卫衣\",\n",
    "    \"裤子\": \"裤子\",\n",
    "    \"夹克\": \"夹克\",\n",
    "    \"外套\": \"外套\",\n",
    "    \"T恤\": \"T恤\",\n",
    "    \"衬衫\": \"衬衫\"\n",
    "}\n",
    "\n",
    "# 定义店铺类型（以名称中包含的关键词来区分）\n",
    "store_types = [\"旗舰店\", \"专营店\"]\n",
    "\n",
    "# 初始化存储结果的字典\n",
    "category_counts_by_store = {store_type: {} for store_type in store_types}\n",
    "\n",
    "# 统计各类别在不同店铺类型下的订单数量\n",
    "for store_type in store_types:\n",
    "    for category, keyword in target_categories.items():\n",
    "        # 筛选店铺名称包含对应关键词且商品名称中包含对应关键词的订单，统计数量\n",
    "        count = data[(data[\"店铺名称\"].str.contains(store_type, na=False)) &\n",
    "                     (data[\"商品名称\"].str.contains(keyword, na=False))].shape[0]\n",
    "        category_counts_by_store[store_type][category] = count\n",
    "\n",
    "# 打印统计结果（格式化输出，清晰展示）\n",
    "for store_type in store_types:\n",
    "    print(\"=\" * 40)\n",
    "    print(f\"ERP订单数据 - 包含{store_type}的店铺 商品类别订单数量统计\")\n",
    "    print(\"=\" * 40)\n",
    "    for category, count in category_counts_by_store[store_type].items():\n",
    "        print(f\"{category:>4}: {count:>3} 个订单\")\n",
    "    print(\"=\" * 40)\n",
    "    total_count = sum(category_counts_by_store[store_type].values())\n",
    "    print(f\"{'总计':>4}: {total_count:>3} 个订单\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fec388fe-4713-4d89-bd36-b1e0b833a3c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 设置中文字体\n",
    "plt.rcParams['axes.unicode_minus'] = False    # 解决负号显示问题\n",
    "# 数据准备\n",
    "flagship_count = 511\n",
    "total_count = 1000\n",
    "percentage = flagship_count / total_count * 100\n",
    "\n",
    "# 创建画布\n",
    "fig, ax = plt.subplots(figsize=(6, 6), facecolor='darkblue')\n",
    "\n",
    "# 绘制圆环图\n",
    "wedges, texts, autotexts = ax.pie(\n",
    "    [percentage, 100 - percentage],\n",
    "    colors=['#8A2BE2', '#FF69B4', '#2F4F4F'],  # 渐变色彩（紫色、粉色、深灰色）\n",
    "    wedgeprops=dict(width=0.3, edgecolor='none'),  # 圆环宽度、无边框\n",
    "    startangle=90,\n",
    "    autopct='%1.0f%%',\n",
    "    pctdistance=0.85,\n",
    "    textprops=dict(color='white', fontsize=20, weight='bold')\n",
    ")\n",
    "\n",
    "# 设置标题和说明文字\n",
    "ax.set_title('2024—2025年订单结构 - 旗舰店订单占比', color='white', fontsize=16, pad=20)\n",
    "ax.text(0, 0, '旗舰店订单占比', color='white', fontsize=12, ha='center', va='center', weight='bold')\n",
    "ax.text(0, -0.2, f'总计：{total_count}个订单\\n旗舰店：{flagship_count}个订单', color='white', fontsize=10, ha='center', va='center')\n",
    "\n",
    "# 隐藏坐标轴\n",
    "ax.axis('equal')\n",
    "plt.axis('off')\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f3ea40c-4597-4cd5-9cb6-7539bb18d6d5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
